Resum:

The goal of this thesis is to investigate the influence of current modulation in the dynamics of the low-frequency fluctuations (LFF) regime induced by optical feedback in semiconductor lasers. In this regime the laser output exhibits apparently random and sudden dropouts that, in some statistical properties, are similar to excitable neuronal spikes. Long time series containing tens of thousands of LFF dropouts were experimentally acquired and simulated, using the Lang and Kobayashi model, under different conditions. By detecting the individual dropouts, the intensity time series were transformed in series of inter-spike intervals (ISI). We then analyzed the ISI sequences by using a symbolic method of analysis capable of unveil serial correlations in data sets, known as ordinal symbolic analysis. Our findings reveal the existence of a hierarchical and clustered organization of ordinal patterns in the ISI series.
When the laser is subject to periodical external forcing, through modulation of the injection current, we identify clear changes in the dynamics as the increase of the modulation amplitude induces deterministic-like behavior in the system. When the modulation frequency is varied, the change in the statistics of the various symbols is empirically shown to be related to specific changes in the ISI distribution, which arise due to different noisy phase-locking regimes.
We also investigated how the spike rate is affected by the modulation, for different parameters that determine the natural (without modulation) spike rate. When the intrinsic spike dynamics is slow, fast modulation can produce faster spikes. When the intrinsic dynamics is already fast, modulation cannot induce much faster spikes. Similar effects were observed in the spike correlations: we found that higher natural spike rates wash out the effects of the modulation in the spike correlations. Simulations of the Lang and Kobayashi model are shown to be in good agreement with the experimental observations.
The results reported in this thesis may be important to the use of semiconductor lasers as optical spiking neurons in information processing networks inspired by biological ones, and more generally, to the analysis of serial correlations in spiking excitable systems. Future work may include investigations of how correlations that encode an external signal spread in a small network of semiconductor lasers.